Deep brain stimulation (DBS) is an effective neurosurgical procedure for the treatment of neurological and neuropsychiatric disorders. The procedure involves placing one or more leads of electrodes into the brain to modulate pathological activity patterns with various forms of electrical stimulation. Successful treatment can be characterized by both symptom suppression and lack of stimulation-induced side effects. Such success requires accurate DBS lead placement as well as spatially targeted stimulation settings to avoid activating neural regions or pathways that induce, for example, adverse motor, sensory, and/or cognitive side effects for the patient.
Conventional designs of DBS leads (for example, the Medtronic Model 3387/3389) use four cylindrical electrodes to deliver current in an omnidirectional manner around the lead. An improvement to this design is to enable the steering of current delivery through electrodes segmented both along and around the DBS lead. Such DBS arrays (DBSAs) are especially useful in cases of imprecise implantation of DBS leads and for small or complex-shaped brain targets that are surrounded by regions and axonal fiber tracts that can elicit side effects when stimulated. However, with a higher number of electrodes available for stimulation, identifying therapeutic stimulation settings through trial-and-error programming is not readily feasible in a clinical setting.
Conventional programming of DBS settings works much like an optometrist performing a vision examination. A clinician will manually test many stimulation settings and evaluate the patient's response to each in order to determine the best one to use. This process can take hours within a single clinical visit and often several clinical visits over the course of weeks to months to optimize. There is a need for efficient, effective, and safe methods of operating, controlling, and/or programming DBSA settings, as conventional approaches to programming DBSAs have numerous drawbacks.
Conventional feedback-based systems for programming DBS settings have embedded technology to log and analyze patient response information or certain biomarkers (such as features in brain waves) in order to inform and update stimulation configurations. Sometimes a rating/ranking system is in place to determine the best configuration based on these responses/biomarkers.
Conventional brain mapping has been used by some researchers who have compiled intraoperative microelectrode stimulation data and mapped them onto a human brain atlas. An efficacy probability map is thus created by assigning each location on the brain atlas a probability for delivering therapeutic stimulation. The probability assignment is based on overlapping spatial distributions with therapeutic stimulation sites at the center of each distribution. Finally, the efficacy probability map (brain atlas) is nonlinearly warped onto the patient's MRI and used to determine the electrode settings that may deliver the best therapy. This approach is entirely based on empirical patient data.
Conventional patient anatomy-based computational neuron models can be used to predict the best stimulating electrode settings for modulating a particular pathway or pathways within the brain. A therapeutic target volume or pathway in the brain is segmented and reconstructed from the patient's MRI data. The volume is populated with simulated model biophysical neurons or grid coordinates and virtual stimulation is applied to them. The tissue enclosed, or pathways encompassed, by the activated volume under virtual DBS provide seed points to then understand the network of modulated brain regions. Large numbers of simulations are run in order to account for the different stimulating electrode configurations, neuron types and orientations, as well as relative locations between electrodes and neurons. The solutions for each simulation are stored in a large lookup table. Given a new target volume for stimulation, the pre-compiled database can be searched for the setting that gives the most overlap between the solution and the target volume.
A previous proposal, disclosed in co-owned U.S. patent application Ser. No. 15/291,628 to Xiao et al. (filed Oct. 12, 2016), includes a programming approach that identifies the electrode configuration that maximizes the overall likelihood that a region or pathway of interest will be activated. This is done by determining the theoretical maximum limits of excitatory influence at each location in a target volume and then tailoring the stimulation setting to try to achieve maximal proximity to that limit. Convex optimization is an efficient approach that can be used to identify the single best electrode configuration that maximizes likelihood of activation. The disclosure of U.S. patent application Ser. No. 15/291,628 is incorporated by reference herein in its entirety.
Conventional approaches for programming DBS systems cannot scale well to DBSAs with more than a handful of electrodes. Manual and feedback-based programming methods can be tailored to the patient but can take too much time and resources to implement effectively. Mapping methods are limited by the availability of a sufficiently rich source database and do not take the unique structure of a patient's brain tissue into account. Many conventional computational models require vast computational resources that may not be present in a clinical setting. Other, more computationally efficient methods that rely on convex optimization are limited by the assumption that a single optimal solution exists.
The need exists, therefore, for improved methods and systems for programming DBS systems.